# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings

from pycocotools.coco import COCO
import platform
arch = platform.machine()
if arch == 'x86_64':
    from mmdet.datasets import decrypt
elif arch == 'aarch64':
    from mmdet.datasets import decrypt_aarch64

import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash

import sys
sys.path.append('../anomaly_detection')

from mmdet import __version__
from mmdet.apis import init_random_seed, set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.utils import (collect_env, get_device, get_root_logger,
                         replace_cfg_vals, rfnext_init_model,
                         setup_multi_processes, update_data_root)


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    parser.add_argument('--config', help='train config file path')
    parser.add_argument('--work-dir', help='the dir to save logs and models')
    parser.add_argument(
        '--resume-from', help='the checkpoint file to resume from')
    parser.add_argument(
        '--auto-resume',
        action='store_true',
        help='resume from the latest checkpoint automatically')
    parser.add_argument(
        '--no-validate',
        action='store_true',
        help='whether not to evaluate the checkpoint during training')
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        '--gpus',
        type=int,
        help='(Deprecated, please use --gpu-id) number of gpus to use '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='(Deprecated, please use --gpu-id) ids of gpus to use '
        '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed training)')
    parser.add_argument('--seed', type=int, default=None, help='random seed')
    parser.add_argument(
        '--diff-seed',
        action='store_true',
        help='Whether or not set different seeds for different ranks')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file (deprecate), '
        'change to --cfg-options instead.')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument(
        '--auto-scale-lr',
        action='store_true',
        help='enable automatically scaling LR.')

    parser.add_argument(
        '--data-path',  help='dataset path')
    parser.add_argument(
        '--batchsize',
        type=int,
        default=8,
        help='training batch size')
    parser.add_argument(
        '--epoch',
        type=int,
        default=2,
        help='training epoch')
    parser.add_argument(
        '--warmup_iters',
        type=int,
        default=500,
        help='training warmup_iters')
    parser.add_argument(
        '--lr',
        type=float,
        default=0.001,
        help='learning rate')
    parser.add_argument('--image_width', 
        type=int, 
        help='input image width')
    parser.add_argument('--image_height', 
        type=int, 
        help='input image height')

    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    if args.options and args.cfg_options:
        raise ValueError(
            '--options and --cfg-options cannot be both '
            'specified, --options is deprecated in favor of --cfg-options')
    if args.options:
        warnings.warn('--options is deprecated in favor of --cfg-options')
        args.cfg_options = args.options

    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)

    # replace the ${key} with the value of cfg.key
    cfg = replace_cfg_vals(cfg)

    # update data root according to MMDET_DATASETS
    update_data_root(cfg)

    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    if args.auto_scale_lr:
        if 'auto_scale_lr' in cfg and \
                'enable' in cfg.auto_scale_lr and \
                'base_batch_size' in cfg.auto_scale_lr:
            cfg.auto_scale_lr.enable = True
        else:
            warnings.warn('Can not find "auto_scale_lr" or '
                          '"auto_scale_lr.enable" or '
                          '"auto_scale_lr.base_batch_size" in your'
                          ' configuration file. Please update all the '
                          'configuration files to mmdet >= 2.24.1.')

    # set multi-process settings
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    
    # 修改训练参数
    if args.resume_from is not None:
        cfg.load_from = os.path.join(args.resume_from,"latest.pth")
    if args.batchsize is not None:
        cfg.data.samples_per_gpu = args.batchsize
    if args.epoch is not None:
        cfg.runner.max_epochs = args.epoch
    if args.warmup_iters is not None:
        cfg.lr_config.warmup_iters = args.warmup_iters
    if args.lr is not None:
        cfg.optimizer.lr = args.lr

    # 修改数据集
    datasets_path = []
    if args.data_path is not None:
        if ',' in args.data_path:
            for path_item in args.data_path.split(','):
                datasets_path.append(path_item)
        else:
            datasets_path.append(args.data_path)
    
    new_annotations_path = [os.path.join(dataset_path, 'annotations/instances_annotations.json') for dataset_path in datasets_path]
    for dataset_path, new_annotation_path in zip(datasets_path, new_annotations_path):
        #coco_config=COCO(new_annotation_path)
        coco_config=None
        while coco_config is None:
            try:
                coco_config=COCO(new_annotation_path)
            except:
                pass
        cfg.train_dataset.img_prefix = os.path.join(dataset_path,"images")
        cfg.train_dataset.ann_file =  new_annotation_path
        cfg.train_dataset.pipeline = cfg.train_pipeline

        cfg.test_dataset.img_prefix = os.path.join(dataset_path,"images")
        cfg.test_dataset.ann_file =  new_annotation_path
        cfg.test_dataset.pipeline = cfg.test_pipeline

        cfg.classes = ()
        for cat in coco_config.cats.values():
            cfg.classes = cfg.classes + tuple([cat['name']])

        cfg.model.bbox_head.num_classes = len(coco_config.getCatIds())

        cfg.train_dataset.classes = cfg.classes
        cfg.test_dataset.classes = cfg.classes

        cfg.train_concat_dataset.datasets.append(copy.deepcopy(cfg.train_dataset))
        cfg.test_concat_dataset.datasets.append(copy.deepcopy(cfg.test_dataset))
        print(cfg.train_concat_dataset.datasets)
        print("******************************************************************************")
    cfg.data.train = cfg.train_concat_dataset
    cfg.data.val = cfg.test_concat_dataset
    cfg.data.test = cfg.test_concat_dataset

    # work_dir is determined in this priority: CLI > segment in file > filename
    if args.work_dir is not None:
        # update configs according to CLI args if args.work_dir is not None
        cfg.work_dir = args.work_dir
    elif cfg.get('work_dir', None) is None:
        # use config filename as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join('./work_dirs',
                                osp.splitext(osp.basename(args.config))[0])

    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.auto_resume = args.auto_resume
    if args.gpus is not None:
        cfg.gpu_ids = range(1)
        warnings.warn('`--gpus` is deprecated because we only support '
                      'single GPU mode in non-distributed training. '
                      'Use `gpus=1` now.')
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids[0:1]
        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
                      'Because we only support single GPU mode in '
                      'non-distributed training. Use the first GPU '
                      'in `gpu_ids` now.')
    if args.gpus is None and args.gpu_ids is None:
        cfg.gpu_ids = [args.gpu_id]

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)
        # re-set gpu_ids with distributed training mode
        _, world_size = get_dist_info()
        cfg.gpu_ids = range(world_size)

    # create work_dir
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # dump config
    cfg.dump(osp.join(cfg.work_dir, 'config.py'))
    # init the logger before other steps
    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # init the meta dict to record some important information such as
    # environment info and seed, which will be logged
    meta = dict()
    # log env info
    env_info_dict = collect_env()
    env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
    dash_line = '-' * 60 + '\n'
    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
                dash_line)
    meta['env_info'] = env_info
    meta['config'] = cfg.pretty_text
    # log some basic info
    logger.info(f'Distributed training: {distributed}')
    logger.info(f'Config:\n{cfg.pretty_text}')

    cfg.device = get_device()
    # set random seeds
    seed = init_random_seed(args.seed, device=cfg.device)
    seed = seed + dist.get_rank() if args.diff_seed else seed
    logger.info(f'Set random seed to {seed}, '
                f'deterministic: {args.deterministic}')
    set_random_seed(seed, deterministic=args.deterministic)
    cfg.seed = seed
    meta['seed'] = seed
    meta['exp_name'] = osp.basename(args.config)

    model = build_detector(
        cfg.model,
        train_cfg=cfg.get('train_cfg'),
        test_cfg=cfg.get('test_cfg'))
    model.init_weights()

    # init rfnext if 'RFSearchHook' is defined in cfg
    rfnext_init_model(model, cfg=cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        assert 'val' in [mode for (mode, _) in cfg.workflow]
        val_dataset = copy.deepcopy(cfg.data.val)
        val_dataset.pipeline = cfg.data.train.get(
            'pipeline', cfg.data.train.dataset.get('pipeline'))
        datasets.append(build_dataset(val_dataset))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        '''
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__ + get_git_hash()[:7],
            CLASSES=datasets[0].CLASSES)
        '''
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta)

# python tools/train_apulis.py --gpus 1 --config configs/AD_mlops/AD_mlops_test01.py --work-dir work_dirs/apulis_train --data-path /home/jiankai-cheng/teamdata/gj_datasets/hongjiao_20230817_coco/train --auto-scale-lr --no-validate
if __name__ == '__main__':
    main()
